OpenRaiser/Intern-Atlas huggingface dataset:
An Efficient Manifold Algorithm for Constructive Interference based Constant Envelope Precoding
Venue: arXiv cs.IT 2017
Year: 2017

In this letter, we propose a novel manifold-based algorithm to solve the constant envelope (CE) precoding problem with interference exploitation. For a given power budget, we design the precoded symbols subject to the CE constraints, such that the constructive effect of the multi-user interference (MUI) is maximized. While the objective for the original problem is non-differentiable on the complex plane, we consider the smooth approximation of its real representation, and map it onto a Riemannian manifold. By using the Riemmanian conjugate gradient (RCG) algorithm, a local minimizer can be efficiently found for the problem. The complexity of the algorithm is analytically derived in terms of floating-points operations (flops) per iteration. Numerical results show that the proposed algorithm outperforms the conventional methods on both symbol error rate and computational complexity.
On the Strong Scaling of the Spectral Element Solver Nek5000 on Petascale Systems
Venue: arXiv cs.DC 2017
Year: 2017

The present work is targeted at performing a strong scaling study of the high-order spectral element fluid dynamics solver Nek5000. Prior studies indicated a recommendable metric for strong scalability from a theoretical viewpoint, which we test here extensively on three parallel machines with different performance characteristics and interconnect networks, namely Mira (IBM Blue Gene/Q), Beskow (Cray XC40) and Titan (Cray XK7). The test cases considered for the simulations correspond to a turbulent flow in a straight pipe at four different friction Reynolds numbers $Re_τ$ = 180, 360, 550 and 1000. Considering the linear model for parallel communication we quantify the machine characteristics in order to better assess the scaling behaviors of the code. Subsequently sampling and profiling tools are used to measure the computation and communication times over a large range of compute cores. We also study the effect of the two coarse grid solvers XXT and AMG on the computational time. Super-linear scaling due to a reduction in cache misses is observed on each computer. The strong scaling limit is attained for roughly 5000 - 10,000 degrees of freedom per core on Mira, 30,000 - 50,0000 on Beskow, with only a small impact of the problem size for both machines, and ranges between 10,000 and 220,000 depending on the problem size on Titan. This work aims at being a reference for Nek5000 users and also serves as a basis for potential issues to address as the community heads towards exascale supercomputers.
An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images
Venue: arXiv cs.CV 2017
Year: 2017

Diabetic retinopathy is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms and hemorrhages. In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a CNN are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our system is publicly available online.
A review of quality frameworks in information systems
Venue: arXiv cs.SE 2017
Year: 2017

Quality is a multidimensional concept that has different meanings in different contexts and perspectives. In the domain of Information system, quality is often understood as the result of an IS development process and as the quality of an IS product. Many models and frameworks have been proposed for evaluating IS quality. However, as yet there is not a commonly accepted framework or standard of IS quality. Typically, researchers propose a set of characteristics, so-called quality factors contributing to the quality of IS. Different stakeholders perspectives are resulting in multiple definitions of quality factors of IS. For instance, some approaches are based on the IS delivery process for the selection of quality factors; while some other approaches do not clearly explain the rationale of their selection. Moreover, often relations or impacts among selected quality factors are not taken into account. Quality aspects of information are frequently considered isolated from IS quality. The impact of IS quality on information quality seems to be neglected in most approaches. Our research aims to incorporate these levels, by which we propose an IS quality framework based on IS architecture. Considering user and IS developers perspectives, different quality factors are identified for various abstraction levels. Besides, the presentation on impacts among different quality factors helps to retrieve the root cause of IS defects. Thus, our framework provides a systematic view on quality of information and IS.
LoRaWAN in the Wild: Measurements from The Things Network
Venue: arXiv cs.NI 2017
Year: 2017

The Long-Range Wide-Area Network (LoRaWAN) specification was released in 2015, primarily to support the Internet-of-Things by facilitating wireless communication over long distances. Since 2015, the role-out and adoption of LoRaWAN has seen a steep growth. To the best of our knowledge, we are the first to have extensively measured, analyzed, and modeled the performance, features, and use cases of an operational LoRaWAN, namely The Things Network. Our measurement data, as presented in this paper, cover the early stages up to the production-level deployment of LoRaWAN. In particular, we analyze packet payloads, radio-signal quality, and spatio-temporal aspects, to model and estimate the performance of LoRaWAN. We also use our empirical findings in simulations to estimate the packet-loss.
Constant Query Time $(1 + ε)$-Approximate Distance Oracle for Planar Graphs
Venue: arXiv cs.DS 2017
Year: 2017

We give a $(1+ε)$-approximate distance oracle with $O(1)$ query time for an undirected planar graph $G$ with $n$ vertices and non-negative edge lengths. For $ε>0$ and any two vertices $u$ and $v$ in $G$, our oracle gives a distance $\tilde{d}(u,v)$ with stretch $(1+ε)$ in $O(1)$ time. The oracle has size $O(n\log n ((\log n)/ε+f(ε)))$ and pre-processing time $O(n\log n((\log^3 n)/ε^2+f(ε)))$, where $f(ε)=2^{O(1/ε)}$. This is the first $(1+ε)$-approximate distance oracle with $O(1)$ query time independent of $ε$ and the size and pre-processing time nearly linear in $n$, and improves the query time $O(1/ε)$ of previous $(1+ε)$-approximate distance oracle with size nearly linear in $n$.
Generate Identity-Preserving Faces by Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

Generating identity-preserving faces aims to generate various face images keeping the same identity given a target face image. Although considerable generative models have been developed in recent years, it is still challenging to simultaneously acquire high quality of facial images and preserve the identity. Here we propose a compelling method using generative adversarial networks (GAN). Concretely, we leverage the generator of trained GAN to generate plausible faces and FaceNet as an identity-similarity discriminator to ensure the identity. Experimental results show that our method is qualified to generate both plausible and identity-preserving faces with high quality. In addition, our method provides a universal framework which can be realized in various ways by combining different face generators and identity-similarity discriminator.
Computational Thinking in Patch
Venue: arXiv cs.PL 2017
Year: 2017

With the future likely to see even more pervasive computation, computational thinking (problem-solving skills incorporating computing knowledge) is now being recognized as a fundamental skill needed by all students. Computational thinking is conceptualizing as opposed to programming, promotes natural human thinking style than algorithmic reasoning, complements and combines mathematical and engineering thinking, and it emphasizes ideas, not artifacts. In this paper, we outline a new visual language, called Patch, using which students are able to express their solutions to eScience computational problems in abstract visual tools. Patch is closer to high level procedural languages such as C++ or Java than Scratch or Snap! but similar to them in ease of use and combines simplicity and expressive power in one single platform.
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
Venue: arXiv cs.LG 2017
Year: 2017

Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster. However, current distributed DL implementations can scale poorly due to substantial parameter synchronization over the network, because the high throughput of GPUs allows more data batches to be processed per unit time than CPUs, leading to more frequent network synchronization. We present Poseidon, an efficient communication architecture for distributed DL on GPUs. Poseidon exploits the layered model structures in DL programs to overlap communication and computation, reducing bursty network communication. Moreover, Poseidon uses a hybrid communication scheme that optimizes the number of bytes required to synchronize each layer, according to layer properties and the number of machines. We show that Poseidon is applicable to different DL frameworks by plugging Poseidon into Caffe and TensorFlow. We show that Poseidon enables Caffe and TensorFlow to achieve 15.5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification. Moreover, Poseidon-enabled TensorFlow achieves 31.5x speed-up with 32 single-GPU machines on Inception-V3, a 50% improvement over the open-source TensorFlow (20x speed-up).
Deep Optimization for Spectrum Repacking
Venue: arXiv cs.AI 2017
Year: 2017

Over 13 months in 2016-17 the FCC conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 UHF channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 billion (used to pay down the national debt) after covering costs. A crucial element of the auction design was the construction of a solver, dubbed SATFC, that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote. This paper describes the process by which we built SATFC. We adopted an approach we dub "deep optimization", taking a data-driven, highly parametric, and computationally intensive approach to solver design. More specifically, to build SATFC we designed software that could pair both complete and local-search SAT-encoded feasibility checking with a wide range of domain-specific techniques. We then used automatic algorithm configuration techniques to construct a portfolio of eight complementary algorithms to be run in parallel, aiming to achieve good performance on instances that arose in proprietary auction simulations. To evaluate the impact of our solver in this paper, we built an open-source reverse auction simulator. We found that within the short time budget required in practice, SATFC solved more than 95% of the problems it encountered. Furthermore, the incentive auction paired with SATFC produced nearly optimal allocations in a restricted setting and substantially outperformed other alternatives at national scale.
Optimal Time Reuse in Cooperative D2D Relaying Networks
Venue: arXiv cs.NI 2017
Year: 2017

Device-to-device (D2D) communication has become one important part of the 5G cellular networks particularly due to the booming of proximity-based applications, e.g. D2D relays. However, the D2D relays may create strong interference to nearby users. Thus interference management in a cellular network with D2D relays is critical. In this paper, we study the optimal time reuse patterns in a cellular network with cooperative D2D relays and derive the corresponding optimized relaying strategies. Due to the binary association constraints and the fact that the total number of feasible time reuse patterns increase exponentially with the number of servers, we have a large scale integer programming problem which is formidable to solve. However, we show that we just need to activate a few time reuse patterns in the order of the number of users. Accordingly, a low-complexity algorithm is proposed to find the set of active time reuse patterns and solve this problem in an approximated way. Numerical simulations demonstrate that our scheme can efficiently allocate the time resources and determine the relaying strategies. Furthermore, the proposed scheme offers significant gains relative to the existing ones.
Bicycle Detection Based On Multi-feature and Multi-frame Fusion in low-resolution traffic videos
Venue: arXiv cs.CV 2017
Year: 2017

As a major type of transportation equipments, bicycles, including electrical bicycles, are distributed almost everywhere in China. The accidents caused by bicycles have become a serious threat to the public safety. So bicycle detection is one major task of traffic video surveillance systems in China. In this paper, a method based on multi-feature and multi-frame fusion is presented for bicycle detection in low-resolution traffic videos. It first extracts some geometric features of objects from each frame image, then concatenate multiple features into a feature vector and use linear support vector machine (SVM) to learn a classifier, or put these features into a cascade classifier, to yield a preliminary detection result regarding whether an object is a bicycle. It further fuses these preliminary detection results from multiple frames to provide a more reliable detection decision, together with a confidence level of that decision. Experimental results show that this method based on multi-feature and multi-frame fusion can identify bicycles with high accuracy and low computational complexity. It is, therefore, applicable for real-time traffic video surveillance systems.
Neutron-induced strike: Study of multiple node charge collection in 14nm FinFETs
Venue: arXiv cs.ET 2017
Year: 2017

FinFETs have replaced the conventional bulk CMOS transistors in the sub-20nm technology. One of the key issues to consider is, the vulnerability of FinFET based circuits to multiple node charge collection due to neutron-induced strikes. In this paper, we perform a device simulation based characterization study on representative layouts of 14nm bulk FinFETs in order to study the extent to which multiple transistors are affected. We find that multiple transistors do get affected and the impact can last up to five transistors away (~200nm). We show that the potential of source/drain regions in the neighborhood of the strike is a significant contributing factor. In the case of multi-fin FinFETs, the charge collected per fin is seen to reduce as the number of fins increase. Thus, smaller FinFETs are susceptible to high amounts of charge collection.
Robust Tracking Guidance for Zero Propellant Maneuver
Venue: arXiv cs.SY 2017
Year: 2017

The Zero Propellant Maneuver (ZPM) maneuvers the space station by large angle, utilizing the Control Momentum Gyroscopes (CMGs) only. A robust tracking guidance strategy is proposed to enhance its performance. It is distinguished from the traditional trajectory tracking guidance in that the reference trajectory is adjusted on-line, under the inspiration of eliminating the discrepancy on the total angular momentum of the space station system. The Lyapunov controller is developed to adjust the attitude trajectory and further redesigned for a better performance based on an interesting physical phenomenon, which is taken advantage of by coupling the components of state vector. The adjusted trajectory is then tracked to reach the target states of maneuver. Simulations results show that the disturbance effects arising from initial state errors, parameter uncertainty and modeling errors are attenuated or even eliminated, which verifies the robustness of the guidance strategy.
Scientific document summarization via citation contextualization and scientific discourse
Venue: arXiv cs.CL 2017
Year: 2017

The rapid growth of scientific literature has made it difficult for the researchers to quickly learn about the developments in their respective fields. Scientific document summarization addresses this challenge by providing summaries of the important contributions of scientific papers. We present a framework for scientific summarization which takes advantage of the citations and the scientific discourse structure. Citation texts often lack the evidence and context to support the content of the cited paper and are even sometimes inaccurate. We first address the problem of inaccuracy of the citation texts by finding the relevant context from the cited paper. We propose three approaches for contextualizing citations which are based on query reformulation, word embeddings, and supervised learning. We then train a model to identify the discourse facets for each citation. We finally propose a method for summarizing scientific papers by leveraging the faceted citations and their corresponding contexts. We evaluate our proposed method on two scientific summarization datasets in the biomedical and computational linguistics domains. Extensive evaluation results show that our methods can improve over the state of the art by large margins.
Few-Shot Image Recognition by Predicting Parameters from Activations
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods.
Coordinate Descent Algorithms for Phase Retrieval
Venue: arXiv cs.IT 2017
Year: 2017

Phase retrieval aims at recovering a complex-valued signal from magnitude-only measurements, which attracts much attention since it has numerous applications in many disciplines. However, phase recovery involves solving a system of quadratic equations, indicating that it is a challenging nonconvex optimization problem. To tackle phase retrieval in an effective and efficient manner, we apply coordinate descent (CD) such that a single unknown is solved at each iteration while all other variables are kept fixed. As a result, only minimization of a univariate quartic polynomial is needed which is easily achieved by finding the closed-form roots of a cubic equation. Three computationally simple algorithms referred to as cyclic, randomized and greedy CDs, based on different updating rules, are devised. It is proved that the three CDs globally converge to a stationary point of the nonconvex problem, and specifically, the randomized CD locally converges to the global minimum and attains exact recovery at a geometric rate with high probability if the sample size is large enough. The cyclic and randomized CDs are also modified via minimization of the $\ell_1$-regularized quartic polynomial for phase retrieval of sparse signals. Furthermore, a novel application of the three CDs, namely, blind equalization in digital communications, is proposed. It is demonstrated that the CD methodology is superior to the state-of-the-art techniques in terms of computational efficiency and/or recovery performance.
Exploring the similarity of medical imaging classification problems
Venue: arXiv cs.CV 2017
Year: 2017

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3\% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community.
Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation
Venue: arXiv cs.CL 2017
Year: 2017

We present a framework and its implementation relying on Natural Language Processing methods, which aims at the identification of exercise item candidates from corpora. The hybrid system combining heuristics and machine learning methods includes a number of relevant selection criteria. We focus on two fundamental aspects: linguistic complexity and the dependence of the extracted sentences on their original context. Previous work on exercise generation addressed these two criteria only to a limited extent, and a refined overall candidate sentence selection framework appears also to be lacking. In addition to a detailed description of the system, we present the results of an empirical evaluation conducted with language teachers and learners which indicate the usefulness of the system for educational purposes. We have integrated our system into a freely available online learning platform.